Quasi-Newton Methods: Superlinear Convergence Without Line Searches for Self-Concordant Functions

Understand

We consider the use of a curvature-adaptive step size in gradient-based iterative methods, including quasi-Newton methods, for minimizing self-concordant functions, extending an approach first proposed for Newton's method by Nesterov.

• This step size has a simple expression that can be computed analytically; hence, line searches are not needed.
• We show that using this step size in the BFGS method (and quasi-Newton methods in the Broyden convex class other than the DFP method) results in superlinear convergence for strongly convex self-concordant functions.
• We present numerical experiments comparing gradient descent and BFGS methods using the curvature-adaptive step size to traditional methods on deterministic logistic regression problems, and to versions of stochastic gradient descent on stochastic optimization problems.